---
title: "Building Data Pipelines with dlt MCP and Continue"
description: "Set up an AI-powered data engineering workflow that helps you develop, debug, and inspect dlt data pipelines using natural language commands."
sidebarTitle: "dlt Data Pipelines with Continue"
---

<Card title="What You'll Build" icon="database">
  An AI-powered data pipeline development system that uses Continue's AI agent with dlt
  MCP to inspect pipeline execution, retrieve schemas, analyze datasets, and debug load errors - all through simple natural language prompts
</Card>

## Prerequisites

Before starting, ensure you have:

- Continue account with **Hub access**
  - Read: [Understanding Configs — How to get started with Hub configs](/guides/understanding-configs#how-to-get-started-with-hub-configs)
- Python 3.8+ installed locally
- A dlt pipeline project (or create one during this guide)
- Basic understanding of data pipelines

For all options, first:
<Steps>
<Step title="Install Continue CLI">
    ```bash
    npm i -g @continuedev/cli
    ```
  </Step>

<Step title="Install dlt">
  ```bash
  pip install dlt
  ```
  </Step>
</Steps>
 <Warning>
  To use agents in headless mode, you need a [Continue API key](https://hub.continue.dev/settings/api-keys).
  </Warning>

## dlt MCP Workflow Options

<Card title="🚀 Fastest Path to Success" icon="zap">
  Skip the manual setup and use our pre-built [dlt Agent](https://hub.continue.dev/continuedev/dlt-agent) that includes
      the dlt MCP and optimized data pipeline workflows for more consistent results. You can [remix this agent](/guides/understanding-configs#how-to-get-started-with-hub-configs) to customize it for your specific needs.
</Card>


After ensuring you meet the **Prerequisites** above, you have two paths to get started:

<Tabs>
  <Tab title="⚡ Quick Start (Recommended)">
    <Steps>
      <Step title="Load the Pre-Built Agent">
        Navigate to your pipeline project directory and run:
        ```bash
        cn --agent continuedev/dlt-agent
        ```

        This agent includes:
        - **dlt MCP** pre-configured and ready to use
        - **Pipeline-focused rules** for data engineering best practices
      </Step>

      <Step title="Run Your First Pipeline Inspection">
        Start with a comprehensive pipeline check:
        ```bash
        # TUI mode
        Inspect the execution of my dlt pipeline and summarize the load info, including timing and file sizes.
        ```

        That's it! The agent handles everything automatically.
      </Step>
    </Steps>

    <Info>
      **Why Use the Agent?** The pre-built [dlt Agent](https://hub.continue.dev/continuedev/dlt-agent) provides consistent pipeline development workflows and handles MCP configuration automatically, making it easier to get started with AI-powered data engineering. You can [remix and customize this agent](/guides/understanding-configs#how-to-get-started-with-hub-configs) later to fit your team's specific workflow.
    </Info>

  </Tab>

  <Tab title="🛠️ Manual Setup">
<Steps>
  <Step title="Create a New Agent via the Continue Mission Control">
    Go to the [Continue Mission Control](https://hub.continue.dev) and [create a new agent](https://hub.continue.dev/new?type=agent).
    </Step>

  <Step title="Connect dlt MCP via Continue Mission Control">
    Visit the [dlt MCP on Continue Mission Control](https://hub.continue.dev/dlthub/dlt-mcp) and click **Install** to add it to the agent you created in the step above.

    This will add dlt MCP to your agent's available tools. The Mission Control listing automatically configures the MCP command.

    <Tip>
      **Alternative installation methods:**
      1. **Quick CLI install**: `cn --mcp dlthub/dlt-mcp`
      2. **Manual configuration**: Add the MCP to your `~/.continue/config.json` under the `mcpServers` section

      Once installed, dlt MCP tools become available to your Continue agent for all prompts.
    </Tip>

    <Info>
      The MCP will work with your existing dlt pipelines in your current directory.
    </Info>

  </Step>

  <Step title="Run Your First Pipeline Inspection">
    Start with a comprehensive pipeline check:
    ```bash
    # TUI mode
    cn
    # Then type: Inspect the execution of my dlt pipeline and summarize the load info, including timing and file sizes.
    ```
  </Step>
</Steps>

  </Tab>
</Tabs>

<Accordion title="Agent Requirements">
    To use the pre-built [dlt Agent](https://hub.continue.dev/continuedev/dlt-agent), you need either:
      - **Continue CLI Pro Plan** with the models add-on, OR
      - **Your own API keys** added to Continue Mission Control secrets (same as manual setup)

    The agent will automatically detect and use your configuration along with the pre-configured dlt MCP for pipeline operations.

</Accordion>

---

## dlt MCP vs dlt+ MCP

<Card title="Understanding the Difference" icon="info-circle">
  **dlt MCP** is focused on local pipeline development and inspection. It provides tools to:
  - Inspect pipeline execution and load information
  - Retrieve schema metadata from your local pipelines
  - Query dataset records from destination databases
  - Analyze load errors, timings, and file sizes

  **[dlt+ MCP](https://hub.continue.dev/dlthub/dlt-plus-mcp)** extends these capabilities with cloud-based features for production deployments:
  - Connect to dlt+ Projects and manage deployments
  - Monitor pipeline runs across multiple environments
  - Access centralized logging and observability
  - Collaborate with team members on pipeline development

  For local development and getting started, **[dlt MCP](https://hub.continue.dev/dlthub/dlt-mcp)** is the right choice. Consider **[dlt+ MCP](https://hub.continue.dev/dlthub/dlt-plus-mcp)** when you need production deployment features and team collaboration.
</Card>

---

## Pipeline Development Recipes

Now you can use natural language prompts to develop and debug your dlt pipelines. The Continue agent automatically calls the appropriate dlt MCP tools.

<Info>
You can add prompts to your agent's configuration for easy access in future sessions. Go to your agent in the [Continue Mission Control](https://hub.continue.dev), click **Edit**, and add prompts under the **Prompts** section.
</Info>

<Info>
  **Where to run these workflows:**
  - **IDE Extensions**: Use Continue in VS Code, JetBrains, or other supported IDEs
  - **Terminal (TUI mode)**: Run `cn` to enter interactive mode, then type your prompts
  - **CLI (headless mode)**: Use `cn -p "your prompt"` for headless commands

  **Test in Plan Mode First**: Before running pipeline operations that might make
  changes, test your prompts in plan mode (see the [Plan Mode
  Guide](/guides/plan-mode-guide); press **Shift+Tab** to switch modes in TUI/IDE). This
  shows you what the agent will do without executing it.
  
  To run any of the example prompts below in headless mode, use `cn -p "prompt"`
</Info>

<Info>

  **About the --auto flag**: The `--auto` flag enables tools to run continuously without manual confirmation. This is essential for headless mode where the agent needs to execute multiple tools automatically to complete tasks like pipeline inspection, schema retrieval, and error analysis.

</Info>

### Pipeline Inspection

<Card title="Inspect Pipeline Execution" icon="magnifying-glass">
  Review pipeline execution details including load timing and file sizes.

**Prompt:**
```
Inspect my dlt pipeline execution and provide a summary of the load info.
Show me the timing breakdown and file sizes for each table.
```

</Card>

### Schema Management

<Card title="Retrieve Schema Metadata" icon="diagram-project">
  Get detailed schema information for your pipeline's tables.

**Prompt:**
```
Show me the schema for my users table including all columns,
data types, and any constraints.
```

</Card>

### Data Exploration

<Card title="Query Dataset Records" icon="table">
  Retrieve and analyze records from your destination database.

**Prompt:**
```
Get the last 10 records from my orders table and show me
the distribution of order statuses.
```

</Card>

### Error Debugging

<Card title="Analyze Load Errors" icon="triangle-exclamation">
  Investigate and understand pipeline load errors.

**Prompt:**
```
Check for any load errors in my last pipeline run. If there are errors,
explain what went wrong and suggest fixes.
```

</Card>

### Pipeline Creation

<Card title="Build New Pipeline" icon="plus">
  Create a new dlt pipeline from an API or data source.

**Prompt:**
```
Help me create a new dlt pipeline that loads data from the
JSONPlaceholder API users endpoint into DuckDB.
```

</Card>

### Schema Evolution

<Card title="Handle Schema Changes" icon="code-branch">
  Review and manage schema evolution in your pipelines.

**Prompt:**
```
Check if my pipeline schema has evolved since the last run.
Show me what columns were added or modified.
```

</Card>

## Continuous Data Pipelines with GitHub Actions

  This example demonstrates a **Continuous AI workflow** where data pipeline validation runs automatically in your CI/CD pipeline in headless mode using the [dlt Assistant agent](https://hub.continue.dev/dlthub/dlt-assistant). Consider [remixing this agent](/guides/understanding-configs#how-to-get-started-with-hub-configs) to add your organization's specific validation rules.

### Add GitHub Secrets

Navigate to **Repository Settings → Secrets and variables → Actions** and add:

- `CONTINUE_API_KEY`: Your Continue API key from [hub.continue.dev/settings/api-keys](https://hub.continue.dev/settings/api-keys)
- Any required database credentials for your destination

<Info>
      The workflow uses the pre-built [dlt Agent](https://hub.continue.dev/continuedev/dlt-agent) with `--agent continuedev/dlt-agent`. This agent comes pre-configured with the dlt MCP and optimized rules for pipeline operations. You can [remix this agent](/guides/understanding-configs#how-to-get-started-with-hub-configs) to customize the validation rules and prompts for your specific pipeline requirements.
</Info>

### Create Workflow File

This workflow automatically validates your dlt data pipelines on pull requests using the Continue CLI in [headless mode](/cli/overview#headless-mode%3A-production-automation). It inspects pipeline schemas, checks for errors, and posts a summary report as a PR comment. The workflow can also be triggered manually via `workflow_dispatch`.

Create `.github/workflows/dlt-pipeline-validation.yml` in your repository:

```yaml
name: Data Pipeline Validation with dlt MCP

on:
  pull_request:
    branches: [main]
  workflow_dispatch:

jobs:
  validate-pipeline:
    runs-on: ubuntu-latest
    env:
      CONTINUE_API_KEY: ${{ secrets.CONTINUE_API_KEY }}

    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"

      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
          node-version: "18"

      - name: Install dlt
        run: |
          pip install dlt
          echo "✅ dlt installed"

      - name: Install Continue CLI
        run: |
          npm install -g @continuedev/cli
          echo "✅ Continue CLI installed"

      - name: Validate Pipeline Schema
        run: |
          echo "🔍 Validating pipeline schema..."
          cn --agent continuedev/dlt-agent \
             -p "Inspect the pipeline schema and verify all required tables
                 and columns are present. Flag any missing or unexpected changes." \
             --auto

      - name: Check Pipeline Health
        run: |
          echo "📊 Checking pipeline health..."
          cn --agent continuedev/dlt-agent \
             -p "Analyze the last pipeline run for errors or warnings.
                 Report any issues that need attention." \
             --auto

      - name: Comment Pipeline Report on PR
        if: always() && github.event_name == 'pull_request'
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          REPORT=$(cn --agent continuedev/dlt-agent \
             -p "Generate a concise summary (200 words or less) of:
                 - Pipeline schemas and row counts
                 - Any load errors or warnings
                 - Performance metrics (timing, file sizes)
                 - Recommended improvements" \
             --auto)

          gh pr comment ${{ github.event.pull_request.number }} --body "$REPORT"
```

<Info>
  The dlt MCP works with your local pipeline state. Make sure your CI environment
  has access to the necessary pipeline configuration and credentials.
</Info>

## Pipeline Development Best Practices

Implement automated pipeline quality checks using Continue's rule system. See the [Rules deep dive](/customize/deep-dives/rules) for authoring tips.

<Card title="Schema Validation" icon="check-circle">
  ```bash
  "Before committing pipeline changes, verify the schema
  matches expectations and flag any unexpected modifications."
  ```
</Card>

<Card title="Error Handling" icon="shield-exclamation">
  ```bash
  "When load errors occur, analyze the error details and
  suggest specific code fixes to handle the data issues."
  ```
</Card>

<Card title="Performance Monitoring" icon="gauge-high">
  ```bash
  "Track pipeline execution times and file sizes. Alert if
  performance degrades significantly from baseline."
  ```
</Card>

<Card title="Data Quality" icon="check-double">
  ```bash
  "After each pipeline run, validate row counts and check for
  null values in critical columns."
  ```
</Card>

## Troubleshooting

### Pipeline Not Found

```bash
"Check if there's a dlt pipeline in the current directory.
If not, help me initialize a new pipeline."
```

### Destination Connection Issues

```bash
"Verify the destination connection and credentials for my pipeline.
Test the connection and report any issues."
```

### Schema Inference Problems

<Check>
  **Verification Steps:**
  - dlt MCP is installed via [Continue Mission Control](https://hub.continue.dev/dlthub/dlt-mcp)
  - Pipeline directory is accessible
  - Destination database credentials are configured
  - Pipeline has been run at least once
</Check>

## What You've Built

After completing this guide, you have a complete **AI-powered data pipeline development system** that:

✅ Uses natural language — Simple prompts instead of complex pipeline commands
✅ Debugs automatically — AI analyzes errors and suggests fixes
✅ Runs continuously — Automated validation in CI/CD pipelines
✅ Ensures quality — Pipeline checks prevent bad data from shipping

<Card title="Continuous AI" icon="rocket">
  Your data pipeline workflow now operates at **[Level 2 Continuous
  AI](https://blog.continue.dev/what-is-continuous-ai-a-developers-guide/)** -
  AI handles routine pipeline inspection and debugging with human oversight
  through review and approval of changes.
</Card>

## Next Steps

1. **Inspect your first pipeline** - Try the pipeline inspection prompt on your current project
2. **Debug load errors** - Use the error analysis prompt to fix any issues
3. **Set up CI pipeline** - Add the GitHub Actions workflow to your repo
4. **Create new pipelines** - Use AI to scaffold new data sources
5. **Monitor performance** - Track pipeline execution metrics over time

## Additional Resources

<CardGroup cols={2}>
  <Card title="dlt Documentation" icon="book" href="https://dlthub.com/docs">
    Complete dlt platform documentation
  </Card>
  <Card title="Continue Mission Control" icon="plug" href="https://hub.continue.dev">
    Explore more MCP integrations and agents
  </Card>
  <Card
    title="dlt Blog: MCP Deep Dive"
    icon="newspaper"
    href="https://dlthub.com/blog/deep-dive-assistants-mcp-continue"
  >
    Learn about AI agents, MCP, and Continue integration
  </Card>
  <Card
    title="MCP Concepts"
    icon="puzzle-piece"
    href="https://dlthub.com/blog/deep-dive-assistants-mcp-continue"
  >
    Deep dive into dlt MCP integration
  </Card>
</CardGroup>